Papers by Keyword: EEG

Abstract: Epilepsy is type of neurological disorder characterized by recurrent seizures that may cause injury to self and others. The ability to predict seizure before its occurrence, so that counter measures are considered, would improve the quality of life of epileptic patients. This research work proposes an adaptive seizure prediction approach based on electroencephalography (EEG) signals analysis. We use cross-correlation to estimate synchronization between EEG channels. Abnormal synchronization between brain regions may reveal brain condition and functionality. Two EEG synchronization baselines, normal and pre-seizure, are used to continuously monitor sliding windows of EEG recording to predict the upcoming seizure. The two baselines are continuously updated using distance-based method based on the most recent prediction outcome. Up to 570 hours continuous EEG recording taken from CHB-MIT dataset is used for validating the proposed method. An overall of 84% sensitivity (46 out of 55 seizures are correctly predicted) and 63% specificity are achieved with one hour prediction horizon. The proposed method is suitable to be implemented in mobile or embedded device which has limited processing resources due to its simplicity.

Abstract: In spite of the potentially harmful effects of vibrations on the human body, a new path was recently opened for the use of these mechanical means in the therapeutic field. The stimulation of proprioceptive and exteroceptive sensitivity is the main target in both peripheral (diabetes type 1 and type 2) and central (stroke, Parkinson's disease multiple sclerosis) nervous system disorders, particularly for the recovery and maintenance of functional state. By the way the response to the treatment is highly variable from subject to subject. Our experimental apparatus consists of a virtual reality system "LEAP Motion" which involves the patient in the execution of visuo-manual tasks in a virtual environment while receiving vibrotactile stimulation. We also used a modular 36 channels EEG system and a vibratory stimulation system able of delivering vibratory stimuli perpendicular and tangential to the body surface area.The study evaluation of motor performance and the ability to perform the tasks of visuomotor task assigned, in the presence and absence of vibratory stimulation and in real time, evoked potentials in the cortex.The vibration frequency extended from 5 to 200 Hz and with accelerations between 0.3G and 1,5G with displacement amplitude of about 0.5 mm applied on the affected limb hand. As the frequency, the amplitude and the direction of the vibration may vary we studied the relationship between the characteristics of the stimulus and the perception in the cerebral cortex, or other levels of the nervous system, studying potential models of elicitation of the somatosensory system. In this regard, our study took into account patients with Parkinson's disease and in particular evoked potentials N18 N20 N24 N30 particularly related to tactile stimulus, and indicative of the level of perception and processing in the brain of the Parkinson's patient.

Abstract: In this paper, an experiment of spike detection based mental task with ayes movement stimuli is reported. The approximation of ICA algorithm is required to eliminate artifacts and detect a pike of brain activity according to the given stimuli which are normal, closed, and blinking ayes. A comparison of ICA algorithms based Extended Fourth Order Blind Identification and Algorithm for Multiple Unknown Signal Extraction is tested. The quality of the extracted signals is measured through the value of the signal to interference ratio and signal to distortion ratio. The extracted results indicate that the best spike detection is achieved using AMUSE algorithm.Keywords : EEG , s pike , Independent Component Analysis (ICA).

Abstract: Nanoparticles of titanium dioxide (TiO₂) are widely used nanomaterial with particle size below 100 nanometers TiO₂ is applied as a pigment to provide whiteness to such products as paints, paper, foodstuffs, medicines, toothpastes, etc. However, neurotropic properties of titanium dioxide remains unclear. This work aimed evaluation of neurotoxic effects of titanium dioxide nanoparticles (12 nm particle size) serially administered to Wistar rats in dose of 250 mg/kg for 7 days. Behavioral and physiological observations were registered immediately after treatment. Results showed that nanoTiO₂ particles caused reducing of general motor activity in rats and a shift of the electroencephalogram (EEG) power toward low frequencies of (EEG), while aggressive behavior, and open field behavior did not change. The depressive effect of titanium dioxide nanoparticles on the central nervous system (CNS) observed in our study might be related to neuronal damage caused by an increase in reactive oxygen species (ROS) as well as the impairment of synaptic transmission.

Abstract: Wet gel electrodes are widely used for ECG/EEG monitoring, their low impedance results in high-quality signals. But they have important drawbacks too, such as time-consuming electrode set-up for EEG followed by a painful removal, skin irritation by the gel and signal degradation due to gel drying. Hence various dry electrode types are investigated, such as hard metal electrodes with low impedance but limited patient comfort/safety. We focus on flexible conductive polymer-based electrodes to combine low impedance, user comfort and safety. The composition of the conductive polymers is optimized to improve various properties such as conductivity, which directly affects signal quality and sensitivity to motion artifacts, and mechanical properties of the electrodes, important with respect to patient comfort. Electrode impedance and ECG/EEG signal recordings are evaluated using various polymer compositions and compared to wet gel electrode results. Additive optimization to improve processability of the conductive formulations is performed by dedicated flow studies, and will result in a high electrode fabrication yield. Very promising results are obtained regarding impedance, EEG/ECG signal quality and user comfort.

Abstract: Brain-Computer Interface (BCI) systems support direct communication and control between brain and external devices without use of peripheral nerves system and muscles. BCI can convert electro-encephalogram (EEG) to the control signal to try repairing function for patients. So the study of BCI can improve the life quality of the patients. This system acquires EEG signals due to the left/right hand motor imagery among the normal subjects. For the processing of motor imagery EEG, we adopt the feature extraction method of second order moment in specific frequency band and the feature classification of linear discriminate analysis. Through the analysis of motor imagery EEG, we convert the data results into external control signal to control the movement of the cursor displayed on the computer. The experimental results show that the EEG analysis method makes it feasible and effective for disabled patients communicating with the outside world, and provides the basis for further study of brain-machine interface. Keywords: EEG; motor imagery; cursor movement; second-order moment.

Abstract: Game process is a complex process, the process of the game is reflected in the subject's brain waves will be in matching pannies out of the coin, so to analyze brain waves of people can study the game process, ERP is an important means of EEG studies, through the course of the game the game ERP analysis process characteristics.

Abstract: People in a different state of fatigue, mental status will be significantly different, brain waves are a direct reflection of mental state, and then the brain waves in different states collected will not have a significant difference as well In this paper, Fisher distance to calculate the difference between fatigue characteristics, by the characteristics of the different states to analyze, to use brain waves to reflect the purpose of human fatigue.

Abstract: This article will extracte biological EEG information, extracted by EEG brain-computer interaction module. It has taken advantage of brain-computer interaction techniques, accurately extracted EEG data, modeling the data, analyzed EEG law, and the signal is used in Bluetooth smart car.The human brain with computers and external communications equipment that can detect and record brain activity by brain-computer interaction technology and brain signals. Brain waves are turned into a language that the computer can understand, so as to effectively control the vehicle, or other external devices.

Abstract: In this paper, coupling measure based on mutual mode entropy (MME) was applied to calculating the statistical complexity of the basic alpha rhythm extracted from the electroencephalogram (EEG) signals, which involved two groups of people, the teenager and the adult. The results show that the alpha rhythms extracted from the adult has a higher MME, which means the indication of higher statistical complexity. The following Independent Sample T Test proved that above-mentioned analysis could disclose significant differences among these two signals’ complexity.